Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [4]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [5]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[5]:
<matplotlib.image.AxesImage at 0x7efc9c816d30>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [6]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[6]:
<matplotlib.image.AxesImage at 0x7efc9c786748>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [7]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [8]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    image_input = tf.placeholder(tf.float32, [None, image_height, image_width, image_channels], name="image_input")
    z_input = tf.placeholder(tf.float32, [None, z_dim], name="z_input")
    learning_rate = tf.placeholder(tf.float32, name="learning_rate")
    return image_input, z_input, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [9]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    # hyper-params
    hidden_units = 128
    alpha =  0.01
    
    # reuse variable
    with tf.variable_scope("discriminator", reuse=reuse):
        # flatten image to 1-rank
        flatten_images = tf.contrib.layers.flatten(images)
        # fully connect nn
        dhl_1 = tf.layers.dense(flatten_images, hidden_units, activation=None)
        # leaky ReLU
        dhl_1 = tf.maximum(alpha*dhl_1, dhl_1)
        # compute logits
        logits = tf.layers.dense(dhl_1, 1, activation=None)
        # compute sigmoid
        output = tf.sigmoid(logits)
    
    return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [10]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    hidden_units = 128
    alpha =  0.01
    output_dim = 28*28*out_channel_dim
    
    with tf.variable_scope("generator", reuse=not is_train):
        # fully connect
        ghl_1 = tf.layers.dense(z, hidden_units, activation=None)
        # leaky relu
        ghl_1= tf.maximum(alpha*ghl_1, ghl_1)
        # compute logits
        logits = tf.layers.dense(ghl_1, output_dim, activation=None)
        # compute output
        output = tf.reshape(tf.tanh(logits), [-1, 28, 28, out_channel_dim])
    return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [11]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    # hyper-param
    smooth = 0.1
    
    # generator net
    input_fake = generator(input_z, out_channel_dim, is_train=True)
    
    # discriminator net
    real_output, real_logits = discriminator(input_real, False)
    fake_output, fake_logits = discriminator(input_fake, True)   
    
    # labels
    label_real = tf.ones_like(real_output)*(1-smooth)
    label_fake = tf.zeros_like(real_output)
    
    # discriminator loss function
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=real_logits, labels=label_real))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logits, labels=label_fake))
    d_loss = d_loss_real+d_loss_fake
    
    # generator loss function
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logits, labels=tf.ones_like(fake_logits)))
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [12]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [d for d in t_vars if d.name.startswith("discriminator")]
    g_vars = [g for g in t_vars if g.name.startswith("generator")]
    
    d_model = tf.train.AdamOptimizer(learning_rate, beta1).minimize(d_loss, var_list=d_vars)
    g_model = tf.train.AdamOptimizer(learning_rate, beta1).minimize(g_loss, var_list=g_vars)
    
    return d_model, g_model


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [13]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [28]:
%matplotlib inline
import matplotlib.pyplot as plt

def train(epoch_count, batch_size, z_dim, lr, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param lr: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    losses = []
    image_width, image_height, image_channels = data_shape[1], data_shape[2], data_shape[3]
    
    image_input, z_input, learning_rate =model_inputs(image_width, image_height, image_channels, z_dim)
    d_loss, g_loss = model_loss(image_input, z_input, image_channels)
    d_model, g_model = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            i = 0
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                i += 1
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # run optimizer
                sess.run(d_model, feed_dict={image_input: batch_images, z_input: batch_z, learning_rate: lr})
                sess.run(g_model, feed_dict={z_input: batch_z, learning_rate: lr})
                
                if i%100==0:
                    show_generator_output(sess, 10, z_input, image_channels, data_image_mode)
                
                # At each epoch, get the losses and print them out
                train_loss_d = sess.run(d_loss, {z_input: batch_z, image_input: batch_images})
                train_loss_g = g_loss.eval({z_input: batch_z})
                if i%100==0:
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g)) 
                losses.append((train_loss_d, train_loss_g))# Save losses to view after training
                
        # plot loss value
        losses = np.array(losses)
        plt.plot(losses.T[0], label='Discriminator')
        plt.plot(losses.T[1], label='Generator')
        plt.title("Training Losses")
        plt.legend()
        plt.show()

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [32]:
batch_size = 64
z_dim = 128
learning_rate = 0.0001
beta1 = 0.8 # refer to http://sebastianruder.com/optimizing-gradient-descent/index.html#adam


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 10

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/10... Discriminator Loss: 1.0428... Generator Loss: 0.6927
Epoch 1/10... Discriminator Loss: 0.9957... Generator Loss: 0.7506
Epoch 1/10... Discriminator Loss: 0.9828... Generator Loss: 0.7769
Epoch 1/10... Discriminator Loss: 0.9347... Generator Loss: 0.8687
Epoch 1/10... Discriminator Loss: 0.9577... Generator Loss: 0.8937
Epoch 1/10... Discriminator Loss: 0.9246... Generator Loss: 0.9510
Epoch 1/10... Discriminator Loss: 1.0791... Generator Loss: 0.8282
Epoch 1/10... Discriminator Loss: 1.0942... Generator Loss: 0.8257
Epoch 1/10... Discriminator Loss: 1.1695... Generator Loss: 0.8331
Epoch 2/10... Discriminator Loss: 1.1072... Generator Loss: 0.8899
Epoch 2/10... Discriminator Loss: 1.2334... Generator Loss: 0.8326
Epoch 2/10... Discriminator Loss: 1.2173... Generator Loss: 0.8481
Epoch 2/10... Discriminator Loss: 1.2595... Generator Loss: 0.8148
Epoch 2/10... Discriminator Loss: 1.2266... Generator Loss: 0.8838
Epoch 2/10... Discriminator Loss: 1.3277... Generator Loss: 0.8226
Epoch 2/10... Discriminator Loss: 1.2510... Generator Loss: 0.8985
Epoch 2/10... Discriminator Loss: 1.1330... Generator Loss: 1.0017
Epoch 2/10... Discriminator Loss: 1.3471... Generator Loss: 0.8541
Epoch 3/10... Discriminator Loss: 1.2679... Generator Loss: 0.9306
Epoch 3/10... Discriminator Loss: 1.2812... Generator Loss: 0.9456
Epoch 3/10... Discriminator Loss: 1.3932... Generator Loss: 0.8538
Epoch 3/10... Discriminator Loss: 1.1095... Generator Loss: 1.0708
Epoch 3/10... Discriminator Loss: 1.2890... Generator Loss: 0.9405
Epoch 3/10... Discriminator Loss: 1.1457... Generator Loss: 1.0633
Epoch 3/10... Discriminator Loss: 1.2686... Generator Loss: 0.9502
Epoch 3/10... Discriminator Loss: 1.1465... Generator Loss: 1.0365
Epoch 3/10... Discriminator Loss: 1.1771... Generator Loss: 1.0438
Epoch 4/10... Discriminator Loss: 1.2501... Generator Loss: 0.9794
Epoch 4/10... Discriminator Loss: 1.2406... Generator Loss: 0.9420
Epoch 4/10... Discriminator Loss: 1.1909... Generator Loss: 0.9962
Epoch 4/10... Discriminator Loss: 1.1373... Generator Loss: 1.0578
Epoch 4/10... Discriminator Loss: 1.1360... Generator Loss: 1.0287
Epoch 4/10... Discriminator Loss: 1.1526... Generator Loss: 1.0438
Epoch 4/10... Discriminator Loss: 1.1670... Generator Loss: 0.9723
Epoch 4/10... Discriminator Loss: 1.1544... Generator Loss: 1.0286
Epoch 4/10... Discriminator Loss: 1.2120... Generator Loss: 0.9703
Epoch 5/10... Discriminator Loss: 1.1477... Generator Loss: 1.0704
Epoch 5/10... Discriminator Loss: 1.1100... Generator Loss: 1.0276
Epoch 5/10... Discriminator Loss: 1.1814... Generator Loss: 0.9825
Epoch 5/10... Discriminator Loss: 1.2858... Generator Loss: 0.8824
Epoch 5/10... Discriminator Loss: 1.1144... Generator Loss: 1.0170
Epoch 5/10... Discriminator Loss: 1.1704... Generator Loss: 0.9255
Epoch 5/10... Discriminator Loss: 1.1798... Generator Loss: 0.9122
Epoch 5/10... Discriminator Loss: 1.1281... Generator Loss: 0.9679
Epoch 5/10... Discriminator Loss: 1.1400... Generator Loss: 1.0087
Epoch 6/10... Discriminator Loss: 1.2048... Generator Loss: 0.9410
Epoch 6/10... Discriminator Loss: 1.1486... Generator Loss: 1.0019
Epoch 6/10... Discriminator Loss: 1.2656... Generator Loss: 0.9260
Epoch 6/10... Discriminator Loss: 1.2264... Generator Loss: 0.9680
Epoch 6/10... Discriminator Loss: 1.3436... Generator Loss: 0.8714
Epoch 6/10... Discriminator Loss: 1.2258... Generator Loss: 0.9563
Epoch 6/10... Discriminator Loss: 1.2183... Generator Loss: 0.9106
Epoch 6/10... Discriminator Loss: 1.3558... Generator Loss: 0.8511
Epoch 6/10... Discriminator Loss: 1.2906... Generator Loss: 0.8757
Epoch 7/10... Discriminator Loss: 1.3153... Generator Loss: 0.8529
Epoch 7/10... Discriminator Loss: 1.2788... Generator Loss: 0.8521
Epoch 7/10... Discriminator Loss: 1.2945... Generator Loss: 0.8831
Epoch 7/10... Discriminator Loss: 1.3558... Generator Loss: 0.8449
Epoch 7/10... Discriminator Loss: 1.3120... Generator Loss: 0.8855
Epoch 7/10... Discriminator Loss: 1.2856... Generator Loss: 0.8816
Epoch 7/10... Discriminator Loss: 1.2775... Generator Loss: 0.8924
Epoch 7/10... Discriminator Loss: 1.2784... Generator Loss: 0.8797
Epoch 7/10... Discriminator Loss: 1.3679... Generator Loss: 0.8071
Epoch 8/10... Discriminator Loss: 1.2689... Generator Loss: 0.9069
Epoch 8/10... Discriminator Loss: 1.3272... Generator Loss: 0.8441
Epoch 8/10... Discriminator Loss: 1.2753... Generator Loss: 0.8973
Epoch 8/10... Discriminator Loss: 1.3064... Generator Loss: 0.8651
Epoch 8/10... Discriminator Loss: 1.2664... Generator Loss: 0.8757
Epoch 8/10... Discriminator Loss: 1.3492... Generator Loss: 0.8329
Epoch 8/10... Discriminator Loss: 1.2597... Generator Loss: 0.8726
Epoch 8/10... Discriminator Loss: 1.2421... Generator Loss: 0.8976
Epoch 8/10... Discriminator Loss: 1.2292... Generator Loss: 0.9286
Epoch 9/10... Discriminator Loss: 1.2886... Generator Loss: 0.8857
Epoch 9/10... Discriminator Loss: 1.1286... Generator Loss: 0.9535
Epoch 9/10... Discriminator Loss: 1.1654... Generator Loss: 0.9717
Epoch 9/10... Discriminator Loss: 1.1368... Generator Loss: 0.9825
Epoch 9/10... Discriminator Loss: 1.2211... Generator Loss: 0.9511
Epoch 9/10... Discriminator Loss: 1.1048... Generator Loss: 1.0301
Epoch 9/10... Discriminator Loss: 1.1810... Generator Loss: 0.9401
Epoch 9/10... Discriminator Loss: 1.1654... Generator Loss: 0.9781
Epoch 9/10... Discriminator Loss: 1.1118... Generator Loss: 0.9997
Epoch 10/10... Discriminator Loss: 1.2456... Generator Loss: 0.9312
Epoch 10/10... Discriminator Loss: 1.0509... Generator Loss: 1.0407
Epoch 10/10... Discriminator Loss: 1.3236... Generator Loss: 0.8387
Epoch 10/10... Discriminator Loss: 1.1686... Generator Loss: 1.0436
Epoch 10/10... Discriminator Loss: 1.1451... Generator Loss: 0.9729
Epoch 10/10... Discriminator Loss: 1.1855... Generator Loss: 0.9791
Epoch 10/10... Discriminator Loss: 1.2557... Generator Loss: 0.9195
Epoch 10/10... Discriminator Loss: 1.1290... Generator Loss: 1.0122
Epoch 10/10... Discriminator Loss: 1.1491... Generator Loss: 1.0280

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [34]:
batch_size = 64
z_dim = 128
learning_rate = 0.0001
beta1 = 0.8 # refer to http://sebastianruder.com/optimizing-gradient-descent/index.html#adam


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.1031... Generator Loss: 0.6552
Epoch 1/2... Discriminator Loss: 0.9689... Generator Loss: 0.7929
Epoch 1/2... Discriminator Loss: 0.9212... Generator Loss: 0.9838
Epoch 1/2... Discriminator Loss: 0.7898... Generator Loss: 1.2175
Epoch 1/2... Discriminator Loss: 0.7894... Generator Loss: 1.1688
Epoch 1/2... Discriminator Loss: 0.8182... Generator Loss: 1.1511
Epoch 1/2... Discriminator Loss: 0.7948... Generator Loss: 1.2245
Epoch 1/2... Discriminator Loss: 0.7817... Generator Loss: 1.2234
Epoch 1/2... Discriminator Loss: 0.7584... Generator Loss: 1.2854
Epoch 1/2... Discriminator Loss: 0.7147... Generator Loss: 1.3677
Epoch 1/2... Discriminator Loss: 0.7408... Generator Loss: 1.3675
Epoch 1/2... Discriminator Loss: 0.7259... Generator Loss: 1.2983
Epoch 1/2... Discriminator Loss: 0.6842... Generator Loss: 1.4304
Epoch 1/2... Discriminator Loss: 0.7049... Generator Loss: 1.3725
Epoch 1/2... Discriminator Loss: 0.6918... Generator Loss: 1.4829
Epoch 1/2... Discriminator Loss: 0.6436... Generator Loss: 1.5321
Epoch 1/2... Discriminator Loss: 0.6999... Generator Loss: 1.4156
Epoch 1/2... Discriminator Loss: 0.7410... Generator Loss: 1.3591
Epoch 1/2... Discriminator Loss: 0.6784... Generator Loss: 1.4929
Epoch 1/2... Discriminator Loss: 0.6313... Generator Loss: 1.5717
Epoch 1/2... Discriminator Loss: 0.6779... Generator Loss: 1.5700
Epoch 1/2... Discriminator Loss: 0.6538... Generator Loss: 1.5659
Epoch 1/2... Discriminator Loss: 0.6228... Generator Loss: 1.6848
Epoch 1/2... Discriminator Loss: 0.6489... Generator Loss: 1.7543
Epoch 1/2... Discriminator Loss: 0.6226... Generator Loss: 1.6735
Epoch 1/2... Discriminator Loss: 0.6178... Generator Loss: 1.7168
Epoch 1/2... Discriminator Loss: 0.6710... Generator Loss: 1.6291
Epoch 1/2... Discriminator Loss: 0.6757... Generator Loss: 1.6285
Epoch 1/2... Discriminator Loss: 0.6623... Generator Loss: 1.7610
Epoch 1/2... Discriminator Loss: 0.6541... Generator Loss: 1.7544
Epoch 1/2... Discriminator Loss: 0.6868... Generator Loss: 1.6069
Epoch 2/2... Discriminator Loss: 0.6727... Generator Loss: 1.7103
Epoch 2/2... Discriminator Loss: 0.7015... Generator Loss: 1.7200
Epoch 2/2... Discriminator Loss: 0.7149... Generator Loss: 1.7011
Epoch 2/2... Discriminator Loss: 0.7992... Generator Loss: 1.5311
Epoch 2/2... Discriminator Loss: 0.8013... Generator Loss: 1.5547
Epoch 2/2... Discriminator Loss: 0.8180... Generator Loss: 1.6518
Epoch 2/2... Discriminator Loss: 0.8460... Generator Loss: 1.6149
Epoch 2/2... Discriminator Loss: 0.8187... Generator Loss: 1.6440
Epoch 2/2... Discriminator Loss: 0.8017... Generator Loss: 1.6520
Epoch 2/2... Discriminator Loss: 0.8599... Generator Loss: 1.5770
Epoch 2/2... Discriminator Loss: 0.8219... Generator Loss: 1.5541
Epoch 2/2... Discriminator Loss: 0.7658... Generator Loss: 1.6863
Epoch 2/2... Discriminator Loss: 0.8578... Generator Loss: 1.5065
Epoch 2/2... Discriminator Loss: 0.7462... Generator Loss: 1.7614
Epoch 2/2... Discriminator Loss: 0.8004... Generator Loss: 1.5927
Epoch 2/2... Discriminator Loss: 0.7980... Generator Loss: 1.5731
Epoch 2/2... Discriminator Loss: 0.7345... Generator Loss: 1.6912
Epoch 2/2... Discriminator Loss: 0.7468... Generator Loss: 1.6213
Epoch 2/2... Discriminator Loss: 0.7840... Generator Loss: 1.6519
Epoch 2/2... Discriminator Loss: 0.8435... Generator Loss: 1.4907
Epoch 2/2... Discriminator Loss: 0.7389... Generator Loss: 1.7600
Epoch 2/2... Discriminator Loss: 0.8460... Generator Loss: 1.5090
Epoch 2/2... Discriminator Loss: 0.8821... Generator Loss: 1.3760
Epoch 2/2... Discriminator Loss: 0.7692... Generator Loss: 1.5849
Epoch 2/2... Discriminator Loss: 0.8096... Generator Loss: 1.4851
Epoch 2/2... Discriminator Loss: 0.7894... Generator Loss: 1.4930
Epoch 2/2... Discriminator Loss: 0.8310... Generator Loss: 1.6303
Epoch 2/2... Discriminator Loss: 0.7572... Generator Loss: 1.6795
Epoch 2/2... Discriminator Loss: 0.8353... Generator Loss: 1.5581
Epoch 2/2... Discriminator Loss: 0.7535... Generator Loss: 1.5931
Epoch 2/2... Discriminator Loss: 0.7860... Generator Loss: 1.5503

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.